Model optimization and stabilization using quantum computing

ABSTRACT

Aspects of the disclosure relate to machine learning and quantum computing. A computing platform may receive historical information, which may include feature information and rate of change information. The computing platform may train, a ML model, by inputting the feature information and the rate of change information, which may make the ML model stable against data drift. The computing platform may receive a first query, input the first query into the ML model, to identify a solution to the first query, which may include identifying a first solution, perturbing the first solution a number of times, and ultimately identifying a second solution, more accurate than the first solution. The computing platform may send this second solution to the user device.

BACKGROUND

Aspects of the disclosure relate to machine learning (ML) models anddata drift. In particular, data drift may result in degradation of modelaccuracy. For ML models, data drift may be represented by a change ininput data, which ultimately leads to such degradation. For example,data drift may be caused by upstream process changes, data qualityissues, natural drift, changes in feature relations, and/or otherwise.In some instances, data drift may be costly (both financially andcomputationally) in ML, as it may necessitate the rebuilding of modelsfrom scratch, which may include collecting new data, revalidating thedata, rebuilding the model, and/or productizing the model. As ML modelsare increasingly implemented across various industries, it may beimportant to address inaccuracies of such models resulting from datadrift.

SUMMARY

Aspects of the disclosure provide effective, efficient, scalable, andconvenient technical solutions that address and overcome the technicalproblems associated with determining stable operating points to accountfor data drift in machine learning models. In accordance with one ormore embodiments of the disclosure, a quantum computing platformcomprising at least one processor, a communication interface, and memorystoring computer-readable instructions may receive historicalinformation, which may include feature information and rate of changeinformation, indicating a speed at which the feature information ischanging over time. The quantum computing platform may train a machinelearning (ML) model to provide a ML result in response to a query, whichmay include inputting the feature information and the rate of changeinformation to train the ML model to be stable against data drift of thehistorical information, and may include training a simulated annealingmodel to identify a global optimum solution for the query, which may bebased on the feature information and the rate of change information. Thequantum computing platform may receive, from a user device, a firstquery. The computing platform may input the first query into the MLmodel, which may cause the ML model to identify the ML result, by: 1)identifying a first local optimum solution, 2) perturbing the firstlocal optimum solution a predetermined number of times based on acooling rate of the simulated annealing model, which may cause the MLmodel to identify at least a second local optimum solution, that may bemore accurate than the first local optimum solution, based on thefeature information and the rate of change information, and 3)outputting, by the quantum computing platform, after perturbing thefirst local optimum the predetermined number of times, the second localoptimum solution, which may be the global optimum solution. The quantumcomputing platform may send, to the user device, the global optimumsolution and one or more commands directing the user device to displaythe global optimum solution, which may cause the user device to displaythe global optimum solution. The quantum computing platform may receivefeedback information indicating a level of satisfaction with the globaloptimum solution. The quantum computing platform may input, into the MLmodel, the feedback information, to continually increase accuracy of theML model.

In one or more instances, the global optimum solution may be differentthan a local optimum solution, and the local optimum solution may bebased on the feature information and not the rate of change information.In one or more instances, the historical information may indicatepreference information for a user of the user device over a period oftime.

In one or more examples, the query may be a commercial query directed toan enterprise of the quantum computing platform. In one or moreexamples, the global optimum solution may be a minimum value of a convexdata representation corresponding to the ML model.

In one or more instances, training the ML model to be stable againstdata drift may avoid further manual training of the ML model whilemaintaining accuracy of the ML model despite the data drift. In one ormore instances, training the ML model may consume an amount of computingresources that exceeds those available on a standard computer processingunit (CPU).

In one or more instances, inputting the feature information to train theML may further include selecting a subset of the feature information foruse in training the ML model, where training the ML model using thesubset of the feature information may consume less processing power thantraining the ML model using the feature information. In one or moreinstances, inputting the feature information to train the ML model mayfurther include: 1) identifying one or more features, of the featureinformation, with a largest impact on the ML model, and 2) inputting,into the ML model, the identified one or more features and theircorresponding rate of change information.

In one or more examples, inputting the rate of change information totrain the ML model may further include: 1) identifying, one or morefeatures, of the feature information, with a largest corresponding rateof change information; and 2) inputting, into the ML model, theidentified one or more features and the corresponding rate of changeinformation.

These features, along with many others, are discussed in greater detailbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIGS. 1A-1B depict an illustrative computing environment for modeloptimization and stabilization using quantum computing in accordancewith one or more example embodiments;

FIGS. 2A-2C depict an illustrative event sequence for model optimizationand stabilization using quantum computing in accordance with one or moreexample embodiments;

FIG. 3 depicts an illustrative method for model optimization andstabilization using quantum computing in accordance with one or moreexample embodiments;

FIG. 4 depicts an illustrative graphical user interface for modeloptimization and stabilization using quantum computing in accordancewith one or more example embodiments; and

FIGS. 5 and 6 depict illustrative system diagrams for model optimizationand stabilization using quantum computing in accordance with one or moreexample embodiments.

DETAILED DESCRIPTION

In the following description of various illustrative embodiments,reference is made to the accompanying drawings, which form a parthereof, and in which is shown, by way of illustration, variousembodiments in which aspects of the disclosure may be practiced. In someinstances, other embodiments may be utilized, and structural andfunctional modifications may be made, without departing from the scopeof the present disclosure.

It is noted that various connections between elements are discussed inthe following description. It is noted that these connections aregeneral and, unless specified otherwise, may be direct or indirect,wired or wireless, and that the specification is not intended to belimiting in this respect.

As a brief introduction to the concepts described further herein, one ormore aspects of the disclosure describe a system and method for stableoperating point determination to address data and/or concept drift. Datadrift may be one of the top reasons that model accuracy degrades overtime. For ML models, data drift may be the change in model input datathat leads to model performance degradation. Monitoring data drift mayhelp to detect these model performance issues. Causes of data drift mayinclude: 1) upstream process changes (e.g., a sensor being replaced thatchanges the units of measurement from inches to centimeters), 2) dataquality issues (e.g., a broken sensor always reading 0), 3) naturaldrift in the data (such as seasonal changes), 4) change in relationbetween features (e.g., covariate shift), and/or other causes.

Data drift may be a costly affair in ML because it may force models tobe rebuilt from scratch. This may entail collecting new data,revalidating the data, and rebuilding/productizing the model. In thisdisclosure, a method of choosing and creating a model that is stableagainst data drifts, and that automatically corrects for the datadrifts, is described herein.

ML methods may be described as an optimization problem. In regressiontype ML problems, the ultimate goal may be to fit a continuous curvethrough the test data that minimizes error. In the case ofclassification problems, the ultimate goal may be to divide the data orthe solution space into clusters so that the error function ofclassification may be minimized.

ML is also a mixed (e.g., discrete and continuous) domain ofoptimization problems with a multitude of variables. In this case, theproblem may be classified as a non-deterministic, polynomial-timehardness (“NP-hard”) problem, for which there does not exist anefficient algorithm for finding the global optimal solution, which isshown, for example, in diagram 505, which is illustrated in FIG. 5 . AllML methods therefore settle down for a suboptimal solution that is alocal optimal, using some suboptimal algorithm such as greedy method,steepest descent, hill climbing, and/or other methods. However, theselocal suboptimal solutions might not be optimized for stability againstdata drift.

Consider the cusp point 610 shown on the left of diagram 605, which isillustrated in FIG. 6 . This may be a stable solution point, since evenif the solution drifts a bit it will roll back to the minimal point.This type of solution may be considered locally stable since thesolution point itself is locally optimal. If the drift is too much,however, then the solution may need recalibration. Now consider thesaddle point 615 on the right of diagram 605. This might not beconsidered a stable operating point. Although a drift in the x value maystill bring it back to the minimal point, a drift in the y value maytake it further and further away from the saddle point. Accordingly, theproblem of finding a stable solution point may be to determine a localoptima that may tolerate all drifts along the feature dimensions.

The drift or a change of a feature may be defined by the derivative ofthe features. In diagram 605, for example, the features of the rightside may be defined as dz/dx for the variable x, or dz/dy for thevariable y. In the discrete data domain, they may be approximated asDELTA(x) or DELTA(y). In order to find a stable optimal point, not onlythe feature vectors may be optimized, but also their derivatives.

In one embodiment, all the feature vectors along with their derivativesmay be added for the optimization method, and then a stable optimizationpoint may be obtained by any of the classical ML algorithms—followed byhyper parameter optimization. In another embodiment, all feature vectorsalong with their derivatives, as well as some of the higher orderderivatives, may be used for added stability of the operating point. Inanother embodiment, only a selected group of features may be used forchoosing the derivatives or higher order derivatives. This may be toincrease the efficiency of the algorithm. The features whose derivativesare chosen may be further identified as those having the highest impacton the stability and more susceptible to data drift. In a furtherenhancement of this embodiment, the selected feature vectors with thehighest impact on the stability and more susceptible to data drift maybe identified automatically using another ML algorithm. Accordingly,described herein is a system and method to obtain a locally stableoptimal solution for the ML method against data drift by adding one ormore order of derivatives of one or more feature vectors along withthose feature vectors to find a stable optimal solution.

FIGS. 1A-1B depict an illustrative computing environment that implementsmodel optimization and stabilization using quantum computing inaccordance with one or more example embodiments. Referring to FIG. 1A,computing environment 100 may include one or more computer systems. Forexample, computing environment 100 may include quantum modeloptimization platform 102, and information storage system 103, a clientdevice 104, and an administrator computing device 105.

As described further below, quantum model optimization platform 102 maybe a computer system that includes one or more computing devices (e.g.,servers, server blades, or the like) and/or other computer components(e.g., processors, memories, communication interfaces) that may train,host, and/or otherwise refine a stable ML model. In some instances, thequantum model optimization platform 102 may be a quantum computingdevice configured for processing that may exceed limits of a standardcomputer processing unit.

Information storage system 103 may include one or more computing devices(e.g., servers, server blades, and/or other devices) and/or othercomputer components (e.g., processors, memories, communicationinterfaces). In some instances, the information storage system 103 maystore information that may be used to train the ML model hosted by thequantum model optimization platform 102 (e.g., demographic information,social networking information, income data, credit ratings, paymenthistory information, preference information, and/or other information).

Client device 104 may be a mobile device, tablet, smartphone, laptopcomputer, desktop computer, and/or other computing device that may beused to submit a query or other request (e.g., request for arecommendation, or other request). In some instances, the client device104 may be configured to access an application that may be configured toprompt a user for the request, and/or to direct requests to the quantummodel optimization platform 102. In some instances, the client device104 may be configured to display one or more user interfaces (e.g.,query interfaces, recommendation interfaces, and/or other interfaces).

Administrator computing device 105 may be a mobile device, tablet,smartphone, laptop computer, desktop computer, and/or other computingdevice that may be used to perform initial training of the ML modelhosted by the quantum model optimization platform 102. For example, theadministrator computing device 105 may be used to configure datasets,parameters, rules, and/or other information of the ML model.

Computing environment 100 also may include one or more networks, whichmay interconnect quantum model optimization platform 102, informationstorage system 103, client device 104, administrator computing device105, and/or other systems. For example, computing environment 100 mayinclude a network 101 (which may interconnect, e.g., quantum modeloptimization platform 102, information storage system 103, client device104, administrator computing device 105, and/or other systems).

In one or more arrangements, quantum model optimization platform 102,information storage system 103, client device 104, and/or administratorcomputing device 105 may be any type of computing device capable ofsending and/or receiving requests and processing the requestsaccordingly. For example, quantum model optimization platform 102,information storage system 103, client device 104, administratorcomputing device 105 and/or the other systems included in computingenvironment 100 may, in some instances, be and/or include servercomputers, desktop computers, laptop computers, tablet computers, smartphones, or the like that may include one or more processors, memories,communication interfaces, storage devices, and/or other components. Asnoted above, and as illustrated in greater detail below, any and/or allof quantum model optimization platform 102, information storage system103, client device 104, and/or administrator computing device 105, may,in some instances, be special-purpose computing devices configured toperform specific functions.

Referring to FIG. 1B, quantum model optimization platform 102 mayinclude one or more processors 111, memory 112, and communicationinterface 113. A data bus may interconnect processor 111, memory 112,and communication interface 113. Communication interface 113 may be anetwork interface configured to support communication between quantummodel optimization platform 102 and one or more networks (e.g., network101, or the like). Memory 112 may include one or more program moduleshaving instructions that when executed by processor 111 cause quantummodel optimization platform 102 to perform one or more functionsdescribed herein and/or one or more databases that may store and/orotherwise maintain information which may be used by such program modulesand/or processor 111. In some instances, the one or more program modulesand/or databases may be stored by and/or maintained in different memoryunits of quantum model optimization platform 102 and/or by differentcomputing devices that may form and/or otherwise make up quantum modeloptimization platform 102. For example, memory 112 may have, host,store, and/or include quantum model optimization module 112 a, quantummodel optimization database 112 b, and machine learning engine 112 c.

Quantum model optimization module 112 a may have instructions thatdirect and/or cause quantum model optimization platform 102 to executeadvanced techniques to provide model stability and/or account for datadrift as discussed in greater detail below. Quantum model optimizationdatabase 112 b may store information used by quantum model optimizationmodule 112 a and/or quantum model optimization platform 102 inapplication of advanced techniques to provide model stability and/oraccount for data drift, and/or in performing other functions. Machinelearning engine 112 c may train, host, and/or otherwise refine a stableML model, which may be robust against data drift. In some instances, themachine learning engine 112 c may iteratively and/or continuously refinethe ML model so as to continuously improve model accuracy.

FIGS. 2A-2C depict an illustrative event sequence for model optimizationand stabilization using quantum computing in accordance with one or moreexample embodiments. Referring to FIG. 2A, at step 201, the informationstorage system 103 may establish a connection with the quantum modeloptimization platform 102. For example, the information storage system103 may establish a first wired or wireless data connection to link theinformation storage system 103 to the quantum model optimizationplatform 102 (e.g., in preparation for sending historical information).In some instances, the information storage system 103 may identifywhether a connection is already established with the quantum modeloptimization platform 102. If a connection is already established withthe quantum model optimization platform 102, the information storagesystem 103 might not re-establish the connection. If a connection is notyet established with the quantum model optimization platform 102, theinformation storage system 103 may establish the first connection asdescribed herein.

At step 202, the information storage system 103 may send historicalinformation (e.g., feature information) to the quantum modeloptimization platform 102. For example, the information storage system103 may send historical information to the quantum model optimizationplatform 102 while the first connection is established. For example, insending the historical information, the information storage system 103may send information about a plurality of individuals (e.g., customersof an enterprise organization corresponding to the quantum modeloptimization platform 102, and/or other individuals) that may be used totrain a ML model at the quantum model optimization platform 102 toprovide recommendations to this plurality of individuals). In someinstances, in sending the historical information, the informationstorage system 103 may send e.g., demographic information, socialnetworking information, income data, credit ratings, payment historyinformation, preference information, and/or other information.

At step 203, the quantum model optimization platform 102 may receive thehistorical information (e.g., feature information) sent at step 202. Forexample, the quantum model optimization platform 102 may receive thehistorical information via the communication interface 113 and while thefirst connection is established.

At step 204, the quantum model optimization platform 102 may train a MLmodel to output recommendation information (e.g., to customers of anenterprise corresponding to the quantum model optimization platform 102,and/or other individuals) automatically or in response to a query. Insome instances, to train the ML model, the quantum model optimizationplatform 102 may identify, for different types of the historicalinformation (e.g., feature information), a rate of change (e.g., a rateat which each type of the historical information is changing for eachindividual). The quantum model optimization platform 102 may then inputthese rates of change into the ML model along with the historicalinformation. By training the ML model on both the historical information(e.g., feature information) and the rates of change, the quantum modeloptimization platform 102 may train a model that is robust and stableagainst data drift, without necessitating collection of new data,revalidation of the data, and/or rebuilding and/or productizing the MLmodel detecting drift in the corresponding data.

In some instances, in training the ML model, the quantum modeloptimization platform 102 may train a simulated annealing model toidentify a global optimum solution as the model output (e.g., based onthe feature information and the rate of change information). Forexample, the quantum model optimization platform 102 may set a coolingschedule (e.g., automatically—based on an amount of data available forthe ML model, a use case, the type of feature information, the rate ofchange information, and/or other information—and/or based on user inputfrom the administrator computing device), which may indicate apredetermined number of times that a local optimal solution, identifiedby the ML model, should be perturbed in an attempt to identify theglobal optimum solution. For example, the quantum model optimizationplatform 102 may train the ML model not to settle on simply a localoptimal solution, but rather a global optimum solution (e.g., as isreflected in diagram 505).

At step 205, the information storage system 103 may send updatedinformation (e.g., feature information) to the quantum modeloptimization platform 102. For example, the information storage system103 may send information similar to the historical information sent atstep 202, but that has deviated from the values of the historicalinformation (e.g., representative of data drift). In some instances, theinformation storage system 103 may send the updated information whilethe first connection is established.

At step 206, the quantum model optimization platform 102 may receive theupdated information, sent at step 205. For example, the quantum modeloptimization platform 102 may receive the updated information via thecommunication interface 113 and while the first connection isestablished.

Referring to FIG. 2B, at step 207, the quantum model optimizationplatform 102 may further refine the ML model based on the updatedinformation (e.g., feature information). For example, the quantum modeloptimization platform 102 may further identify rate of changeinformation for the various types of information on a per user basis,and update the ML model accordingly. In some instances, thediscrepancies between the updated information and the historicalinformation may represent data drift, indicating change in preferences,circumstances, and/or otherwise for various individuals.

In some instances, the initial training and/or refining of the ML modelmay involve communication with an administrator computing device 105(e.g., to obtain manually input parameters, data, values, rules, and/orother information). For example, in some instances, the quantum modeloptimization platform 102 may communicate with the administratorcomputing device to select a subset of the historical and/or newinformation (e.g., feature information), using feature engineering, totrain the ML model. In some instances, the feature engineering may beautomatically performed by the quantum model optimization platform 102and/or based on input from the administrator computing device 105. Insome instances, in performing the feature engineering, the quantum modeloptimization platform 102 and/or administrator computing device 105 mayidentify one or more features, of the feature information, with alargest impact on the ML model, and may input the identified one or morefeatures and their corresponding rate of change information into the MLmodel. Additionally or alternatively, in performing the featureengineering, the quantum model optimization platform 102 and/oradministrator computing device 105 may identify one or more features, ofthe feature information, with a largest corresponding rate of change,and may input the identified one or more features and the correspondingrate of change information into the ML model. In doing so, the quantummodel optimization platform 102 may consume less processing power intraining the ML model than would otherwise be consumed if all of thehistorical and/or new information (e.g., feature information) were to beused. In some instances, similar feature engineering may be performed atstep 204.

In some instances, the ML model may remain stable and robust (e.g., thusremaining accurate), without further manual training, despite the datadrift corresponding to the updated information. For example, the MLmodel may have been trained, at step 204, using rate of changeinformation, which may allow the ML model to automatically develop overtime while maintaining accuracy and without needing manual recalibrationbased on the updated information.

In some instances, training the ML model, whether at step 204 or 207,the quantum model optimization platform 102 may execute processes thatmay consume an amount of computing resources that exceeds thoseavailable on a standard computer processing unit (CPU), but that may besupported by a quantum computing resource.

At step 208, the client device 104 may establish a connection withquantum model optimization platform 102. For example, the client device104 may establish a second wired or wireless data connection with thequantum model optimization platform 102 to link the client device 104 tothe quantum model optimization platform 102 (e.g., in preparation forsending queries). In some instances, the client device 104 may identifywhether or not a connection is already established with the quantummodel optimization platform 102. If a connection is already establishedwith the quantum model optimization platform 102, the client device 104might not re-establish the connection. If a connection is not yetestablished with the quantum model optimization platform 102, the clientdevice 104 may establish the second connection as described herein.

At step 209, the client device 104 may send a query to the quantum modeloptimization platform 102. For example, the client device 104 may send aquery to the quantum model optimization platform 102 prompting for arecommendation (e.g., product recommendation, service recommendation,answer to a question, chatbot interaction, and/or other information). Insome instances, the client device 104 may send a query directed to acommercial enterprise, server, product, or the like (e.g., rather than apure research query). In some instances, the client device 104 may sendthe query to the quantum model optimization platform 102 while thesecond connection is established.

At step 210, the quantum model optimization platform 102 may receive thequery sent at step 209. For example, the quantum model optimizationplatform 102 may receive the query via the communication interface 113and while the second connection is established. In some instances, steps209 and 210 may be optional, and the quantum model optimization platform102 may automatically identify, without being prompted by the clientdevice 104, that a recommendation or other information, produced by theML model, should be provided to the client device 104 (e.g.,preconfigured recommendations and/or other information sent at apredetermined interval, or the like).

At step 211, the quantum model optimization platform 102 may input thequery into the ML model to produce a ML result. For example, the quantummodel optimization platform 102 may identify, by comparing the query tothe information stored in the model (e.g., both historical and updatedinformation and change rates) a first local optimum solution. Afteridentifying the first local optimum solution, the quantum modeloptimization platform 102 may perturb the first local optimum solution,and identify a new local optimum solution based on the modification(which may ultimately be the same as the first local optimum solution,or may be a different local optimum solution, more accurate (e.g.,corresponding to a greater confidence value) than the first localoptimum solution). The quantum model optimization platform 102 mayrepeat this process of perturbing an identified local optimum andidentifying a new optimum a number of times (e.g., one or more times)corresponding to a cooling schedule (which may e.g., include apredetermined number of times that the process should be repeated), andmay decrease a counter, initially corresponding to the number of times,by one each time the process is repeated. A final optimum solution(e.g., a global optimum) may be identified by the quantum modeloptimization platform 102 once the count reaches zero. For example, thequantum model optimization platform 102 may execute a simulatedannealing model, which may identify a number of local optimum results,based on a cooling schedule, before settling on a final global optimum,which may, e.g., be more accurate than the previously identified localoptimums. In some instances, in settling on the global optimum solution,the quantum model optimization platform 102 may identify an ML resultcorresponding to a minimum value of a convex data representationcorresponding to the ML model.

In some instances, in identifying the local optimums individually, thequantum model optimization platform 102 might not consider the rate ofchange information. However, by perturbing the local optimums (e.g.,based on the rate of change information), the quantum model optimizationplatform 102 may ultimately take into account the rate of changeinformation in identifying the global optimum solution, which may, e.g.,be different than the first local optimum identified.

In some instances, in identifying the ML result, the quantum modeloptimization platform 102 may execute processes that may consume anamount of computing resources that exceeds those available on a standardCPU, but that may be supported by a quantum computing resource.

Referring to FIG. 2C, at step 212, the quantum model optimizationplatform 102 may send the ML result to the client device 104. Forexample, the quantum model optimization platform 102 may send the MLresult to the client device 104 via the communication interface 113 andwhile the second connection is established. In some instances, thequantum model optimization platform 102 may also send one or morecommands directing the client device 104 to display the ML result.

At step 213, the client device 104 may receive the ML result sent atstep 212. For example, the client device 104 may receive the ML resultwhile the second connection is established. In some instances, theclient device 104 may also receive the one or more commands directingthe client device 104 to display the ML result.

At step 214, based on or in response to the one or more commandsdirecting the client device 104 to display the ML result, the clientdevice 104 may display the ML result. For example, the client device 104may display a graphical user interface similar to graphical userinterface 405, which is shown in FIG. 4 (e.g., providing arecommendation and/or other information output from the ML model).

At step 215, the quantum model optimization platform 102 may refine theML model based on the identified ML result and/or user feedback. Forexample, the quantum model optimization platform 102 may feed the MLresult back into the ML model to continuously and dynamically improveaccuracy of the ML model based on the analysis performed at step 211.Additionally or alternatively, the quantum model optimization platform102 may receive user feedback (e.g., from the client device 104)indicating a level of satisfaction with the ML result. In doing so, thequantum model optimization platform 102 may establish a dynamic feedbackloop, in which this user feedback is input back into the ML model tocontinuous and dynamically refine the ML model to increase accuracy.

FIG. 3 depicts an illustrative method for model optimization andstabilization using quantum computing in accordance with one or moreexample embodiments. Referring to FIG. 3 , at step 305, a computingplatform having at least one processor, a communication interface, andmemory may retrieve historical information. At step 310, the computingplatform may identify whether any new information has been receivedand/or whether data drift has been detected. If new information and/ordata drift has not been detected, the computing platform may proceed tostep 320. If new information and/or data drift has been detected, thecomputing platform may proceed to step 315.

At step 315, the computing platform may refine the ML model based on thenew information. At step 320, the computing platform may receive a queryfrom a client device. At step 325, the computing platform may identify aML result responsive to the query. At step 330, the computing platformmay send a ML result and one or more commands directing the clientdevice to display the ML result. At step 335, the computing platform mayrefine the ML model based on the ML result and/or user feedback.

One or more aspects of the disclosure may be embodied in computer-usabledata or computer-executable instructions, such as in one or more programmodules, executed by one or more computers or other devices to performthe operations described herein. Generally, program modules includeroutines, programs, objects, components, data structures, and the likethat perform particular tasks or implement particular abstract datatypes when executed by one or more processors in a computer or otherdata processing device. The computer-executable instructions may bestored as computer-readable instructions on a computer-readable mediumsuch as a hard disk, optical disk, removable storage media, solid-statememory, RAM, and the like. The functionality of the program modules maybe combined or distributed as desired in various embodiments. Inaddition, the functionality may be embodied in whole or in part infirmware or hardware equivalents, such as integrated circuits,application-specific integrated circuits (ASICs), field programmablegate arrays (FPGA), and the like. Particular data structures may be usedto more effectively implement one or more aspects of the disclosure, andsuch data structures are contemplated to be within the scope of computerexecutable instructions and computer-usable data described herein.

Various aspects described herein may be embodied as a method, anapparatus, or as one or more computer-readable media storingcomputer-executable instructions. Accordingly, those aspects may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, an entirely firmware embodiment, or an embodiment combiningsoftware, hardware, and firmware aspects in any combination. Inaddition, various signals representing data or events as describedherein may be transferred between a source and a destination in the formof light or electromagnetic waves traveling through signal-conductingmedia such as metal wires, optical fibers, or wireless transmissionmedia (e.g., air or space). In general, the one or morecomputer-readable media may be and/or include one or more non-transitorycomputer-readable media.

As described herein, the various methods and acts may be operativeacross one or more computing servers and one or more networks. Thefunctionality may be distributed in any manner, or may be located in asingle computing device (e.g., a server, a client computer, and thelike). For example, in alternative embodiments, one or more of thecomputing platforms discussed above may be combined into a singlecomputing platform, and the various functions of each computing platformmay be performed by the single computing platform. In such arrangements,any and/or all of the above-discussed communications between computingplatforms may correspond to data being accessed, moved, modified,updated, and/or otherwise used by the single computing platform.Additionally or alternatively, one or more of the computing platformsdiscussed above may be implemented in one or more virtual machines thatare provided by one or more physical computing devices. In sucharrangements, the various functions of each computing platform may beperformed by the one or more virtual machines, and any and/or all of theabove-discussed communications between computing platforms maycorrespond to data being accessed, moved, modified, updated, and/orotherwise used by the one or more virtual machines.

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one or more of the steps depicted in theillustrative figures may be performed in other than the recited order,and one or more depicted steps may be optional in accordance withaspects of the disclosure.

What is claimed is:
 1. A quantum computing platform comprising: at leastone processor; a communication interface communicatively coupled to theat least one processor; and memory storing computer-readableinstructions that, when executed by the at least one processor, causethe quantum computing platform to: receive, by the quantum computingplatform, historical information, wherein the historical informationincludes feature information and rate of change information, indicatinga speed at which the feature information is changing over time; train,by the quantum computing platform, a machine learning (ML) model toprovide a ML result in response to a query, and wherein training the MLmodel comprises inputting the feature information and the rate of changeinformation to train the ML model to be stable against data drift of thehistorical information, wherein training the ML model comprises traininga simulated annealing model to identify a global optimum solution forthe query, and wherein the global optimum solution is based on thefeature information and the rate of change information; receive, by thequantum computing platform and from a user device, a first query; input,by the quantum computing platform, the first query into the ML model,wherein inputting the first query into the ML model causes the ML modelto identify the ML result, wherein identifying the ML result comprises:identifying a first local optimum solution; perturbing the first localoptimum solution a predetermined number of times based on a cooling rateof the simulated annealing model, wherein perturbing the first localoptimum solution the predetermined number of times causes the ML modelto identify at least a second local optimum solution, wherein the atleast a second local optimum solution is more accurate than the firstlocal optimum solution, based on the feature information and the rate ofchange information, and outputting, by the quantum computing platform,after perturbing the first local optimum solution the predeterminednumber of times, the second local optimum solution, wherein the secondlocal optimum solution comprises the global optimum solution; send, bythe quantum computing platform and to the user device, the globaloptimum solution and one or more commands directing the user device todisplay the global optimum solution, wherein sending the one or morecommands directing the user device to display the global optimumsolution causes the user device to display the global optimum solution;receive, by the quantum computing platform, feedback informationindicating a level of satisfaction with the global optimum solution; andinput, into the ML model, the feedback information, to continuallyincrease accuracy of the ML model.
 2. The quantum computing platform ofclaim 1, wherein the global optimum solution is different than a localoptimum solution, and wherein the local optimum solution is based on thefeature information and not the rate of change information.
 3. Thequantum computing platform of claim 1, wherein the historicalinformation indicates preference information for a user of the userdevice over a period of time.
 4. The quantum computing platform of claim1, wherein the query comprises a commercial query directed to anenterprise of the quantum computing platform.
 5. The quantum computingplatform of claim 1, wherein the global optimum solution comprises aminimum value of a convex data representation corresponding to the MLmodel.
 6. The quantum computing platform of claim 1, wherein trainingthe ML model to be stable against data drift avoids further manualtraining of the ML model while maintaining accuracy of the ML modeldespite the data drift.
 7. The quantum computing platform of claim 1,wherein training the ML model consumes an amount of computing resourcesthat exceeds those available on a standard computer processing unit(CPU).
 8. The quantum computing platform of claim 1, wherein inputtingthe feature information to train the ML model further comprises:selecting a subset of the feature information for use in training the MLmodel, wherein training the ML model using the subset of the featureinformation consumes less processing power than training the ML modelusing the feature information.
 9. The quantum computing platform ofclaim 8, wherein inputting the feature information to train the ML modelfurther comprises: identifying one or more features, of the featureinformation, with a largest impact on the ML model; and inputting, intothe ML model, the identified one or more features and theircorresponding rate of change information.
 10. The quantum computingplatform of claim 8, wherein inputting the rate of change information totrain the ML model further comprises: identifying, one or more features,of the feature information, with a largest corresponding rate of changeinformation; and inputting, into the ML model, the identified one ormore features and the corresponding rate of change information.
 11. Thecomputing platform of claim 1, wherein the predetermined number of timescomprises two or more.
 12. A method comprising: at a quantum computingplatform comprising at least one processor, a communication interface,and memory: receiving, by the quantum computing platform, historicalinformation, wherein the historical information includes featureinformation and rate of change information, indicating a speed at whichthe feature information is changing over time; training, by the quantumcomputing platform, a machine learning (ML) model to provide a ML resultin response to a query, and wherein training the ML model comprisesinputting the feature information and the rate of change information totrain the ML model to be stable against data drift of the historicalinformation, wherein training the ML model comprises training asimulated annealing model to identify a global optimum solution for thequery, and wherein the global optimum solution is based on the featureinformation and the rate of change information; receiving, by thequantum computing platform and from a user device, a first query;inputting, by the quantum computing platform, the first query into theML model, wherein inputting the first query into the ML model causes theML model to identify the ML result, wherein identifying the ML resultcomprises: identifying a first local optimum solution; perturbing thefirst local optimum solution a predetermined number of times based on acooling rate of the simulated annealing model, wherein perturbing thefirst local optimum solution the predetermined number of times causesthe ML model to identify at least a second local optimum solution,wherein the at least a second local optimum solution is more accuratethan the first local optimum solution, based on the feature informationand the rate of change information, and outputting, by the quantumcomputing platform, after perturbing the first local optimum solutionthe predetermined number of times, the second local optimum solution,wherein the second local optimum solution comprises the global optimumsolution; sending, by the quantum computing platform and to the userdevice, the global optimum solution and one or more commands directingthe user device to display the global optimum solution, wherein sendingthe one or more commands directing the user device to display the globaloptimum solution causes the user device to display the global optimumsolution; receiving, by the quantum computing platform, feedbackinformation indicating a level of satisfaction with the global optimumsolution; and inputting, into the ML model, the feedback information, tocontinually increase accuracy of the ML model.
 13. The method of claim12, wherein the global optimum solution is different than a localoptimum solution, and wherein the local optimum solution is based on thefeature information and not the rate of change information.
 14. Themethod of claim 12, wherein the historical information indicatespreference information for a user of the user device over a period oftime.
 15. The method of claim 12, wherein the query comprises acommercial query directed to an enterprise of the quantum computingplatform.
 16. The method of claim 12, wherein the global optimumsolution comprises a minimum value of a convex data representationcorresponding to the ML model.
 17. The method of claim 12, whereintraining the ML model to be stable against data drift avoids furthermanual training of the ML model while maintaining accuracy of the MLmodel despite the data drift.
 18. The method of claim 12, whereintraining the ML model consumes an amount of computing resources thatexceeds those available on a standard computer processing unit (CPU).19. The method of claim 12, wherein inputting the feature information totrain the ML model further comprises: selecting a subset of the featureinformation for use in training the ML model, wherein training the MLmodel using the subset of the feature information consumes lessprocessing power than training the ML model using the featureinformation.
 20. One or more non-transitory computer-readable mediastoring instructions that, when executed by a quantum computing platformcomprising at least one processor, a communication interface, andmemory, cause the quantum computing platform to: receive, by the quantumcomputing platform, historical information, wherein the historicalinformation includes feature information and rate of change information,indicating a speed at which the feature information is changing overtime; train, by the quantum computing platform, a machine learning (ML)model to provide a ML result in response to a query, and whereintraining the ML model comprises inputting the feature information andthe rate of change information to train the ML model to be stableagainst data drift of the historical information, wherein training theML model comprises training a simulated annealing model to identify aglobal optimum solution for the query, and wherein the global optimumsolution is based on the feature information and the rate of changeinformation; receive, by the quantum computing platform and from a userdevice, a first query; input, by the quantum computing platform, thefirst query into the ML model, wherein inputting the first query intothe ML model causes the ML model to identify the ML result, whereinidentifying the ML result comprises: identifying a first local optimumsolution; perturbing the first local optimum solution a predeterminednumber of times based on a cooling rate of the simulated annealingmodel, wherein perturbing the first local optimum solution thepredetermined number of times causes the ML model to identify at least asecond local optimum solution, wherein the at least a second localoptimum solution is more accurate than the first local optimum solution,based on the feature information and the rate of change information, andoutputting, by the quantum computing platform, after perturbing thefirst local optimum solution the predetermined number of times, thesecond local optimum solution, wherein the second local optimum solutioncomprises the global optimum solution; send, by the quantum computingplatform and to the user device, the global optimum solution and one ormore commands directing the user device to display the global optimumsolution, wherein sending the one or more commands directing the userdevice to display the global optimum solution causes the user device todisplay the global optimum solution; receive, by the quantum computingplatform, feedback information indicating a level of satisfaction withthe global optimum solution; and input, into the ML model, the feedbackinformation, to continually increase accuracy of the ML model.